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Azcoaga-Lorenzo A, Fagbamigbe AF, Agrawal U, Black M, Usman M, Lee SI, Eastwood KA, Moss N, Plachcinski R, Nelson-Piercy C, Brophy S, O'Reilly D, Nirantharakumar K, McCowan C. Maternal multimorbidity and preterm birth in Scotland: an observational record-linkage study. BMC Med 2023; 21:352. [PMID: 37697325 PMCID: PMC10496247 DOI: 10.1186/s12916-023-03058-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Multimorbidity is common in women across the life course. Preterm birth is the single biggest cause of neonatal mortality and morbidity. We aim to estimate the prevalence of multimorbidity in pregnant women and to examine the association between maternal multimorbidity and PTB. METHODS This is a retrospective cohort study using electronic health records from the Scottish Morbidity Records. All pregnancies among women aged 15 to 49 with a conception date between 1 January 2014 and 31 December 2018 were included. Multimorbidity was defined as the presence of two or more pre-existing long-term physical or mental health conditions, and complex multimorbidity as the presence of four or more. It was calculated at the time of conception using a predefined list of 79 conditions published by the MuM-PreDiCT consortium. PTB was defined as babies born alive between 24 and less than 37 completed weeks of gestation. We used Generalised Estimating Equations adjusted for maternal age, socioeconomic status, number of previous pregnancies, BMI, and smoking history to estimate the effect of maternal pre-existing multimorbidity. Absolut rates are reported in the results and tables, whilst Odds Ratios (ORs) are adjusted (aOR). RESULTS Thirty thousand five hundred fifty-seven singleton births from 27,711 pregnant women were included in the analysis. The prevalence of pre-existing multimorbidity and complex multimorbidity was 16.8% (95% CI: 16.4-17.2) and 3.6% (95% CI: 3.3-3.8), respectively. The prevalence of multimorbidity in the youngest age group was 10.2%(95% CI: 8.8-11.6), while in those 40 to 44, it was 21.4% (95% CI: 18.4-24.4), and in the 45 to 49 age group, it was 20% (95% CI: 8.9-31.1). In women without multimorbidity, the prevalence of PTB was 6.7%; it was 11.6% in women with multimorbidity and 15.6% in women with complex multimorbidity. After adjusting for maternal age, socioeconomic status, number of previous pregnancies, Body Mass Index (BMI), and smoking, multimorbidity was associated with higher odds of PTB (aOR = 1.64, 95% CI: 1.48-1.82). CONCLUSIONS Multimorbidity at the time of conception was present in one in six women and was associated with an increased risk of preterm birth. Multimorbidity presents a significant health burden to women and their offspring. Routine and comprehensive evaluation of women with multimorbidity before and during pregnancy is urgently needed.
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Affiliation(s)
- Amaya Azcoaga-Lorenzo
- Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK.
- Hospital Rey Juan Carlos, Instituto de Investigación Sanitaria Fundación Jimenez Diaz, Madrid, Spain.
- Research Network On Chronicity, Primary Care and Prevention and Health Promotion, (ISCIII), Madrid, Spain.
| | - Adeniyi Francis Fagbamigbe
- Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Institute of Applied Health Sciences, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, UK
| | - Utkarsh Agrawal
- Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Mairead Black
- Aberdeen Centre for Women's Health Research, School of Medicine, Medical Science and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Muhammad Usman
- Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
| | - Siang Ing Lee
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Kelly-Ann Eastwood
- Centre for Public Health, Queen's University of Belfast, Belfast, UK
- St Michael's Hospital, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Ngawai Moss
- Patient and Public Representative, London, UK
| | | | | | - Sinead Brophy
- Data Science, Medical School, Swansea University, Swansea, UK
| | - Dermot O'Reilly
- Centre for Public Health, Queen's University of Belfast, Belfast, UK
| | | | - Colin McCowan
- Division of Population and Behavioural Sciences, School of Medicine, University of St Andrews, St Andrews, UK
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Hussein N, Hassan R, Fahey MT. Effect of pavement condition and geometrics at signalised intersections on casualty crashes. J Safety Res 2021; 76:276-288. [PMID: 33653560 DOI: 10.1016/j.jsr.2020.12.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 09/14/2020] [Accepted: 12/21/2020] [Indexed: 06/12/2023]
Abstract
INTRODUCTION This study investigated the effects of pavement surface condition and other control factors on casualty crashes at signalized intersections. It involved conducting a before and after study for road surface condition and situational factors. It also included assessing the effects of geometric characteristics on safety performance of signalized intersections post resurfacing to control for the effect of pavement surface condition. Pavement surface condition included roughness, rutting, and skid resistance. The control factors included traffic volume, light and surface moisture condition, and speed limit. The geometric characteristics included approach width, number of lanes, intersection depth, presence of median, presence of shared lane, and presence of bus stop. METHOD To account for the repeated observations of the effect of light and surface moisture conditions in four occasions (day-dry, day-wet, night-dry and night-wet) Generalized Estimating Equation (GEE) with Negative Binomial (NB) and log link function was applied. For each signalized intersection in the sample, condition data are collected for the year before and after the year of surface treatment. Crash data, however, are collected for a minimum of three and maximum of five years before and after treatment years. RESULTS The results show that before treatment, light condition, road surface moisture condition, and skid resistance interaction with traffic volume are the significant contributors to crash occurrence. For after treatment; light condition, road surface moisture condition, their interaction product, and roughness interaction with light condition, surface moisture condition, and traffic volume are the significant contributors. The geometric variables that were found to have significant effects on crash frequency post resurfacing were approach width interactions with presence of shared lane, bus stop, or median. CONCLUSIONS The findings confirm that resurfacing is significant in reducing crash frequency and severity levels. Practical Applications: The study findings would help for better understanding of how geometric characteristics can be improved to reduce crash occurrence.
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Affiliation(s)
- Nasreen Hussein
- Department of Civil and Construction Engineering, Swinburne University of Technology, Victoria, 3122, Australia and University of Duhok, College of Engineering, Civil Department, Kurdistan Region Iraq.
| | - Rayya Hassan
- Department of Health Science and Biostatistics, Swinburne University of Technology, Victoria 3122, Australia.
| | - Michael T Fahey
- Department of Health Science and Biostatistics, Swinburne University of Technology, Victoria, 3122, Australia.
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